Robust and accurate anomaly detection in ECG artifacts using time series motif discovery.

Sivaraks H, Ratanamahatana CA - Comput Math Methods Med (2015)

Bottom Line:
In addition, every step of the algorithm conforms to the interpretation of cardiologists.Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists.Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate.

ABSTRACTElectrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

fig19: Anomaly detection result of the INCARTDB04 dataset by BitClusterDiscord algorithm with L = 203.

Mentions:
On the other hand, BFDD, HOT SAX, and BitClusterDiscord did produce various false alarm results, as shown in Figures 17, 18, and 19. Although the algorithm can still detect anomalous subsequences, they do not properly cover entire anomalous beats.

fig19: Anomaly detection result of the INCARTDB04 dataset by BitClusterDiscord algorithm with L = 203.

Mentions:
On the other hand, BFDD, HOT SAX, and BitClusterDiscord did produce various false alarm results, as shown in Figures 17, 18, and 19. Although the algorithm can still detect anomalous subsequences, they do not properly cover entire anomalous beats.

Bottom Line:
In addition, every step of the algorithm conforms to the interpretation of cardiologists.Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists.Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate.

ABSTRACTElectrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.